Hyperspectral image (HSI) has received considerable attention in the field of target detection due to its powerful ability to capture the spectral information of land covers, and plenty of detection algorithms have been explored. However, these methods generally leverage the difference between the spectrum of the target to be detected and the background spectrum to accomplish target detection, and so are susceptible to the problem of spectral variability. In this article, we propose a global-to-local hierarchical detection algorithm for HSI (G2LHTD). Firstly, extended morphological attribute profile (EMAP) is first used to model global spatial texture information from HSI. Subsequently, a diverse-direction constrained energy minimization (D ² CEM) detector is developed to consider the spatial information within eight neighborhoods around each pixel in HSI, yielding comprehensive local spatial information. More substantially, to effectively discriminate the neighborhood information in diverse directions, we devise an adaptive neighborhood feature aggregation (ANFA) strategy, which will comprehensively evaluate the significance of neighborhood information in diverse directions. As a result, the spatial features of HSI can be comprehensively considered for hyperspectral target detection (HTD). Extensive experiments, conducted on four standard datasets, demonstrate the effectiveness of the proposed method. The codes of this work will be available at https://github.com/zhonghaocheng/G2LHTD_Master for the sake of reproducibility.

Abundant spectral signatures and spatial characteristics embedded in hyperspectral (HS) images enable the fine identification of land covers, attracting plenty of studies on feature extraction and feature utilization. Nevertheless, the high representative spectral and spatial features in the HS cube are unevenly distributed, which is failed to consider by many current methods. To conquer this shortcoming, we rethink the feature extraction of HS images from an anisotropic perspective and propose a novel model called grid network (GNet) for HS image classification. Beyond representing spectral-spatial features in three classic paradigms (simultaneously, hierarchically, and separately), GNet is capable of learning them in two new processes: multi-stage and multi-path. In this way, spectral and spatial features can be fully and balanced explored. More significantly, to make full use of low- and high-level features and avoid the existing semantic gap, we devise a spectral-spatial cross-level feature fusion module to model the relation between them. Extensive experiments, implemented on three HS datasets, demonstrate that the proposed GNet enables to acquire promising classification performance compared to state-of-the-art methods. The codes of this work will be available at https://github.com/zhonghaochen/GNet_Master for the sake of reproducibility.

Recently, hyperspectral image classification based on deep learning has achieved considerable attention. Many convolutional neural network classification methods have emerged and exhibited superior classification performance. However, most methods focus on extracting features by using fixed convolution kernels and layer-wise representation, resulting in feature extraction singleness. Additionally, the feature fusion process is rough and simple. Numerous methods get accustomed to fusing different levels of features by stacking modules hierarchically, which ignore the combination of shallow and deep spectral-spatial features. In order to overcome the preceding issues, a novel multiscale dual-branch feature fusion and attention network is proposed. Specifically, we design a multiscale feature extraction (MSFE) module to extract spatial-spectral features at a granular level and expand the range of receptive fields, thereby enhancing the MSFE ability. Subsequently, we develop a dual-branch feature fusion interactive module that integrates the residual connection's feature reuse property and the dense connection's feature exploration capability, obtaining more discriminative features in both spatial and spectral branches. Additionally, we introduce a novel shuffle attention mechanism that allows for adaptive weighting of spatial and spectral features, further improving classification performance. Experimental results on three benchmark datasets demonstrate that our model outperforms other state-of-the-art methods while incurring the lower computational cost.

Deep learning (DL) has recently risen to prominence in hyperspectral target detection (HTD). Nevertheless, how to tackle the extreme training sample imbalance together with achieving target highlighting and background suppression is challenging. Additionally, due to the spectral redundancy of hyperspectral imagery (HSI), it is a new course for HTD through band selection (BS) to retain crucial bands thereupon improving the subsequent detection performance. Accordingly, we propose a DL-based BS-HTD (DLBSTD) algorithm, incorporating DL-based BS with DL-based HTD for the first time. Most significantly, a multi-depth and multi-branch network (MDBN) for HTD based on a novel BS method is proposed. First of all, the BS method including an alternating local-global reconstruction network (ALGRN) and a correlation measurement strategy provides representative bands containing key target information for MDBN. For the training sample imbalance of MDBN, we develop a BS-based method to select multifarious representative background training samples and propose a target band random substitution (TBRS) strategy to augment an ample target training set. Lastly, the MDBN composed of a multi-depth feature extraction (MDFE) module, three fusion strategies, and the parallel local convolution and gated recurrent unit (Conv-GRU) fully taps the spectral feature relationships to highlight targets and suppress backgrounds. Compared with nine competitive HTD algorithms, we carry out plentiful experiments on four classical datasets exhibiting that the proposed DLBSTD has strong generalization and salient detection performance of target highlighting and background suppression.